Zobrazeno 1 - 10
of 196
pro vyhledávání: '"Caraffini, Fabio"'
Hyperparameter optimization is a crucial problem in Evolutionary Computation. In fact, the values of the hyperparameters directly impact the trajectory taken by the optimization process, and their choice requires extensive reasoning by human operator
Externí odkaz:
http://arxiv.org/abs/2408.02451
Autor:
Pérez-Pérez, Juan F., Gómez, Pablo Isaza, Bonet, Isis, Sánchez-Pinzón, María Solange, Caraffini, Fabio, Lochmuller, Christian
Climate risk assessment is becoming increasingly important. For organisations, identifying and assessing climate-related risks is challenging, as they can come from multiple sources. This study identifies and assesses the main climate transition risk
Externí odkaz:
http://arxiv.org/abs/2404.16055
Risk mitigation techniques are critical to avoiding accidents associated with driving behaviour. We provide a novel Multi-Class Driver Distraction Risk Assessment (MDDRA) model that considers the vehicle, driver, and environmental data during a journ
Externí odkaz:
http://arxiv.org/abs/2402.13421
This study investigates the influence of several bound constraint handling methods (BCHMs) on the search process specific to Differential Evolution (DE), with a focus on identifying similarities between BCHMs and grouping patterns with respect to the
Externí odkaz:
http://arxiv.org/abs/2305.12221
New contributions in the field of iterative optimisation heuristics are often made in an iterative manner. Novel algorithmic ideas are not proposed in isolation, but usually as an extension of a preexisting algorithm. Although these contributions are
Externí odkaz:
http://arxiv.org/abs/2304.09524
Evaluating the performance of heuristic optimisation algorithms is essential to determine how well they perform under various conditions. Recently, the BIAS toolbox was introduced as a behaviour benchmark to detect structural bias (SB) in search algo
Externí odkaz:
http://arxiv.org/abs/2304.01869
Autor:
Kuhn, Stefan, Cobas, Carlos, Barba, Agustin, Colreavy-Donnelly, Simon, Caraffini, Fabio, Borges, Ricardo Moreira
This paper presents a proof-of-concept method for classifying chemical compounds directly from NMR data without doing structure elucidation. This can help to reduce time in finding good structure candidates, as in most cases matching must be done by
Externí odkaz:
http://arxiv.org/abs/2211.03173
Autor:
Kononova, Anna V., Vermetten, Diederick, Caraffini, Fabio, Mitran, Madalina-A., Zaharie, Daniela
We argue that results produced by a heuristic optimisation algorithm cannot be considered reproducible unless the algorithm fully specifies what should be done with solutions generated outside the domain, even in the case of simple box constraints. C
Externí odkaz:
http://arxiv.org/abs/2203.03512
Heuristic optimisation algorithms are in high demand due to the overwhelming amount of complex optimisation problems that need to be solved. The complexity of these problems is well beyond the boundaries of applicability of exact optimisation algorit
Externí odkaz:
http://arxiv.org/abs/2105.04693
Structural Bias (SB) is an important type of algorithmic deficiency within iterative optimisation heuristics. However, methods for detecting structural bias have not yet fully matured, and recent studies have uncovered many interesting questions. One
Externí odkaz:
http://arxiv.org/abs/2105.04480